Job
Description
ML Ops Engineer Skills: Expert in Python programming with experience in libraries like NumPy, Pandas etc. Experience in API deployment using frameworks like FastAPI, Flask, Django, Tornado, bottle etc. Total of 3-7 years of experience in managing machine learning projects end-to-end, with last project focused on MLOps. Experience in supporting model builds and model deployment for IDE-based models and autoML tools, experiment tracking, model management, version tracking & model training (Dataiku, Datarobot, Kubeflow, MLflow, neptune.ai), model hyperparameter optimization, model evaluation, and explainability (SHAP, Tensorboard).ss Experience with one of container technologies (Docker, Kubernetes, EKS, ECS). Experience with multiple cloud providers (AWS, GCP, Azure, etc), preferred GCP. Experience with MLOps tools such as ModelDB, Kubeflow, Pachyderm, and Data Version Control (DVC). Monitoring Build & Production systems using automated monitoring and alarm tools. Knowledge of machine learning frameworks: TensorFlow, PyTorch, Keras, Scikit-Learn. MLOps Engineer Responsibilities: Deploying and operationalizing MLOps. Model evaluation and explainability. Model training and automated retraining. Model workflows from onboarding, operations to decommissioning. Model version tracking & governance. Data archival & version management. Model hyperparameter optimization (Good to have). Model and drift monitoring. Creating and using benchmarks, metrics, and monitoring to measure and improve services. Providing best practices and executing POC for automated and efficient model operations at scale. Designing and developing scalable MLOps frameworks to support models based on client requirements. Desirable Education: Deep quantitative/programming background with a degree (Bachelors, Master’s, or Ph.D.) in a highly analytical discipline, like Statistics, Economics, Computer Science, Mathematics, Operations Research, etc.
